1. Field
The present invention relates generally to computer systems and, more specifically, to techniques for inferring consumer affinities based on geolocated shopping behavior.
2. Description of the Related Art
Geolocation analytics platforms are generally used to understand human behavior. Such systems map data about places to geographic locations and then this mapping is used to analyze patterns in human behavior based on people's presence in those geographic locations. For example, researchers may use such systems to understand patterns in health, educational, crime, or political outcomes in geographic areas. And some companies use such systems to understand the nature of their physical locations, analyzing, for instance, the demographics of customers who visit their stores, restaurants, or other facilities. Some companies use such systems to measure and understand the results of TV advertising campaigns, detecting changes in the types of customers who visit stores following a campaign. Some companies use geolocation analytics platforms to target content to geolocations, e.g., selecting content like business listings, advertisements, billboards, mailings, restaurant reviews, and the like, based on human behavior associated with locations to which the content is directed. In many contexts, location can be a useful indicator of human behavior.
Consumer companies spend billions of dollars trying to find new customers and increase interaction with existing customers. Measuring and understanding this is very difficult. Today many companies use fragmented data sets, such as purchase data and demographics to segment customers for analytics and prediction of customer value. Once this is known, many companies make operational decisions, such as making available incentives, selecting new store locations and targeting marketing (media purchase, event sponsorship etc.), based on this data.
Particularly issues arise when analyzing larger populations according to their behavior as consumers. Often, the relevant segments are not known ex ante, so labeled training sets are often unavailable to construct and refine predictive models. Further, adequately processing data indicative of consumer at commercially relevant scales is often beyond the capabilities of many traditional analytical systems. In many cases, the diversity of consumer behavior often warrants relatively long-tailed descriptive models, and model features are, in many cases, only revealed when processing relatively large data sets, e.g., describing behavior of millions of consumers.